Data-Driven Production Logistics – An Industrial Case Study on Potential and Challenges

Abstract

Abstract Production logistics is typically considered a nonvalue-adding activity with a low level of automation and digitalization. However, recent advancements in technology infrastructure for capturing real-time data are key enablers of smart production logistics and are expected to empower companies to adopt data-driven strategies for more responsive, efficient, and sustainable intrasite logistic systems. Still, empirical evidence is lacking on potential and challenges in industrial transitions toward such systems. The objective of this article is to analyze the potential and challenges of adopting data-driven production logistics based on an industrial case study at an international manufacturing company in the pharmaceutical industry. The industrial application is analyzed in relation to established frameworks for data-driven manufacturing, and key technology infrastructures are identified. The potential of adopting a data-driven solution for the industrial case is quantified through simulating a future scenario and relating the results to the five SCOR performance attributes: reliability, responsiveness, agility, cost, and asset management efficiency. The findings show that deploying a data-driven approach can improve the overall performance of the system. The improvements especially concern lead-time, utilization of resources and space, streamlining logistics processes, and synchronization between production and logistics. On the other hand, challenges in adopting this data-driven strategy include a lack of relevant competence, difficulties of creating technological infrastructure and indistinct vision, and issues with integrity. Key contributions of the article include the analysis of a real industrial case for identification of potential and challenges while adopting a smart and data-driven production logistics.